With the increasing of urban population, human activities have caused high-impact land cover and land use changes (LCLUC) in cities.

Comprehensive records of land cover land use (LCLU) dynamics in urban regions are essential for strategic climate change adaption and mitigation as well as sustainable urban development.

A research team led by Prof. HE Xingyuan from the Northeast Institute of Geography and Agroecology (IGA) of the Chinese Academy of Sciences (CAS) developed a systematic remote sensing frame in Google Earth Engine (GEE) for high-resolution (15-m) urban LCLUC mapping. The frame includes Landsat pan-sharpening algorithm and the Multilevel Decision Rule (MDR) classifier.

The researchers used the method to generate annual urban LCLU maps of Changchun, China from 2000 to 2019, which supported to quantify the cause and effects of urban LCLUC. 

They found that the urbanization of Changchun was uneven before and after the year 2009: the built-up land expansion halved from +2.24 %·yr^-1 to +1.30 %·yr^-1, the cropland loss transited from -9.19 %·yr^-1 to -5.29 %·yr^-1, and the green space increment shifted from +2.66 %·yr^-1 to +0.43 %·yr^-1.

The statistical analysis showed that economic development, population increment, and industrial development were major factors driving LCLUC. The built-up land expansion and the green space growth caused wind velocity descent with the finer particle increment.

This method is a first crucial step towards understanding the drivers of change and supporting better decision-making for sustainable urban development and climate change mitigation. 

The study was published in Remote Sensing on July 30. It was supported by the Youth Science Fund Project approved by the Youth Innovation Promotion Association of CAS and the National Natural Science Foundation of China.